Comparison of classification methods for tissue outcome after ischaemic stroke.
Autor: | Tozlu C; Université de Lyon, Lyon, France.; Université Lyon 1, Villeurbanne, France.; Hospices Civils de Lyon, Service de Biostatistique-Bioinformatique, Lyon, France.; CNRS, UMR5558, Laboratoire de Biométrie et de Biologie Évolutive, Équipe Biostatistique-Santé, Villeurbanne, France., Ozenne B; Neurobiology Research Unit, Rigshospitalet, Copenhagen O, Denmark.; Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen K, Denmark., Cho TH; Department of Stroke Medicine and Department of Neuroradiology, Université Lyon 1, CREATIS, CNRS, UMR 5220-INSERM U1044, INSA-Lyon, Hospices Civils de Lyon, Lyon, France., Nighoghossian N; Department of Stroke Medicine and Department of Neuroradiology, Université Lyon 1, CREATIS, CNRS, UMR 5220-INSERM U1044, INSA-Lyon, Hospices Civils de Lyon, Lyon, France., Mikkelsen IK; Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark., Derex L; Department of Stroke Medicine and Department of Neuroradiology, Université Lyon 1, CREATIS, CNRS, UMR 5220-INSERM U1044, INSA-Lyon, Hospices Civils de Lyon, Lyon, France., Hermier M; Department of Stroke Medicine and Department of Neuroradiology, Université Lyon 1, CREATIS, CNRS, UMR 5220-INSERM U1044, INSA-Lyon, Hospices Civils de Lyon, Lyon, France., Pedraza S; Department of Radiology (IDI), Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain., Fiehler J; Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany., Østergaard L; Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark.; Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark., Berthezène Y; Department of Stroke Medicine and Department of Neuroradiology, Université Lyon 1, CREATIS, CNRS, UMR 5220-INSERM U1044, INSA-Lyon, Hospices Civils de Lyon, Lyon, France.; Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark., Baron JC; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.; INSERM U894, Hôpital Sainte-Anne, Université Paris Descartes, Sorbonne Paris Cité, Paris, France., Maucort-Boulch D; Université de Lyon, Lyon, France.; Université Lyon 1, Villeurbanne, France.; Hospices Civils de Lyon, Service de Biostatistique-Bioinformatique, Lyon, France.; CNRS, UMR5558, Laboratoire de Biométrie et de Biologie Évolutive, Équipe Biostatistique-Santé, Villeurbanne, France. |
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Jazyk: | angličtina |
Zdroj: | The European journal of neuroscience [Eur J Neurosci] 2019 Nov; Vol. 50 (10), pp. 3590-3598. Date of Electronic Publication: 2019 Sep 12. |
DOI: | 10.1111/ejn.14507 |
Abstrakt: | In acute ischaemic stroke, identifying brain tissue at high risk of infarction is important for clinical decision-making. This tissue may be identified with suitable classification methods from magnetic resonance imaging data. The aim of the present study was to assess and compare the performance of five popular classification methods (adaptive boosting, logistic regression, artificial neural networks, random forest and support vector machine) in identifying tissue at high risk of infarction on human voxel-based brain imaging data. The classification methods were used with eight MRI parameters, including diffusion-weighted imaging and perfusion-weighted imaging obtained in 55 patients. The five criteria used to assess the performance of the methods were the area under the receiver operating curve (AUC (© 2019 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.) |
Databáze: | MEDLINE |
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